Exploratory Analysis of Demographic Characteristics Where Facilities are Located

Analysis at the Block Group Level

# can use this function to see what variables are available for the given dataset
# listCensusMetadata(name = "pdb/blockgroup", vintage = 2021, type = "variables", group = NULL)


# get census data for the variables listed at the blockgroup level; block level not available 
get_census_data_safe <- function(state_code) {
  region_for_state = paste0("state:", state_code, "+county:*+tract:*")
  result <- tryCatch( 
    {
    getCensus(name = "pdb/blockgroup",
                      vintage = 2021,
                      key = "fbbc8b0d3e53089da7cabc380628d6d46ae00444",
                      vars = c("Block_group",
                               "County", 
                               "URBANIZED_AREA_POP_CEN_2010",
                               "Tot_Population_CEN_2010", 
                               "Tot_Population_ACS_15_19", 
                               "Males_ACS_15_19", 
                               "Females_ACS_15_19",
                               "Median_Age_ACS_15_19", 
                               "Hispanic_ACS_15_19",
                               "NH_White_alone_ACS_15_19",
                               "NH_Blk_alone_ACS_15_19",
                               "NH_AIAN_alone_ACS_15_19",
                               "NH_Asian_alone_ACS_15_19",
                               "NH_NHOPI_alone_ACS_15_19", 
                               "NH_SOR_alone_ACS_15_19",
                               "Pov_Univ_ACS_15_19",
                               "Prs_Blw_Pov_Lev_ACS_15_19",
                               "No_Health_Ins_ACS_15_19",
                               "Tot_Occp_Units_ACS_15_19",
                               "Aggregate_HH_INC_ACS_15_19",
                               "Med_HHD_Inc_BG_ACS_15_19",
                               "Tot_Prns_in_HHD_ACS_15_19",
                               "Renter_Occp_HU_ACS_15_19",
                               "Med_House_Value_BG_ACS_15_19",
                               "pct_ENG_VW_ACS_15_19",
                               "pct_URBANIZED_AREA_POP_CEN_2010",
                               "pct_RURAL_POP_CEN_2010",
                               "pct_Inst_GQ_CEN_2010",
                               "pct_Non_Inst_GQ_CEN_2010",
                               "pct_URBAN_CLUSTER_POP_CEN_2010",
                               "pct_PUB_ASST_INC_ACS_15_19"
                               ),
                      regionin = region_for_state,
                      region = "block group:*")
          } ,
      error=function(e) {
            return()
        } 
      )
  return(result)
}


# get block group level data for every state in state_codes - only run when making changes to the variables included in the function above
# blockgroup_level_data <- map_dfr(state_codes,  ~get_census_data_safe(.x))

# remove of redundant columns
# blockgroup_level_data <- blockgroup_level_data %>% 
#  select(-Block_group, County)

# write_csv(blockgroup_level_data, "./data/blockgroup_level_data.csv")

blockgroup_level_data <- read_csv("./data/blockgroup_level_data.csv")

# get block group number, which is the first digit of the block number 
prison_boundaries_blocks <- prison_boundaries_blocks %>% 
  mutate(block_group = as.numeric(substr(BLOCKCE10, 1,1)))

# join prison boundaries data to the census data 
prison_boundaries_block_census <- left_join(prison_boundaries_blocks, blockgroup_level_data,
                                            by = c("STATEFP10" = "state",
                                                   "COUNTYFP10" = "county",
                                                   "TRACTCE10" = "tract",
                                                   "block_group" = "block_group"))

# reorder TYPE and SECURELVL
prison_boundaries_block_census <- prison_boundaries_block_census %>% 
  mutate(TYPE = factor(TYPE,  
                       levels = c("LOCAL", "COUNTY",
                                  "STATE", "FEDERAL",
                                  "MULTI", "NOT AVAILABLE")),
         SECURELVL = factor(SECURELVL,  levels = c("JUVENILE", "MINIMUM",
                                        "MEDIUM", "MAXIMUM",
                                        "CLOSE", "NOT AVAILABLE")))

# all variables in the dataset 
all_vars <- prison_boundaries_block_census %>% colnames()

Variable definitions are from the documentation for the 2021 Planning Database.

First, we can look at the distributions of the median age in block groups where facilities are located.

Note that we can look at these distributions by block group (where the frequency on the y-axis is the number of block groups) and by facility (where the frequency on the y-axis is the number of facilities). The difference in these approaches is due to the fact that some block groups contain multiple facilities, which we can see by running the line prison_boundaries_block_census %>% group_by(STATE, COUNTY, TRACTCE10, block_group) %>% summarize(n = n()) %>% filter(n > 1 ) %>% nrow(). This shows us that 987 block groups contain multiple facilities.

Distribution of Median Age

Distribution of Median Age in Block Groups
Data from the 2015-2019 ACS
Mean Median
Block Groups in the U.S. 40.5 39.8
Block Groups Where Facilities are Located 39.2 38.4

Distribution of Median Age in Block Groups Where Facilities are Located
by Facility Type
Data from the 2015-2019 ACS
Facility Type Mean Median
LOCAL 37.14 36.50
COUNTY 39.30 38.35
STATE 39.20 38.50
FEDERAL 38.86 39.20
MULTI 35.99 34.30
NOT AVAILABLE 41.45 40.90

Distribution of Median Age in Block Groups Where Facilities are Located
by Facility Security Level
Data from the 2015-2019 ACS
Facility Security Level Mean Median
JUVENILE 38.50 37.50
MINIMUM 39.24 38.80
MEDIUM 38.46 38.30
MAXIMUM 37.73 36.80
CLOSE 38.35 37.75
NOT AVAILABLE 39.56 38.80 
#### MAKING PLOTS BY FACILITY, RATHER THAN BLOCK GROUP
#### FREQUENCY ON Y-AXIS REPRESENTS FACILITIES, SO SOME BLOCK GROUPS ARE COUNTED MULTIPLE TIMES


###### distribution of median age 
prison_boundaries_block_census %>% 
  select(Median_Age_ACS_15_19) %>% 
  ggplot(aes(x = Median_Age_ACS_15_19 )) + 
  geom_histogram() +
  geom_vline(aes(xintercept = median(Median_Age_ACS_15_19, na.rm = TRUE)), 
             color = "darkred") +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Median Age",
       subtitle = "Median of the Distribution Marked in Red\n2015 – 2019 5-year ACS sample data ",
       title = "Distribution of Median Age in Block Groups Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5))

##### distribution of median age faceted by facility type
prison_boundaries_block_census %>% 
  group_by(TYPE) %>%
  mutate(Median = median(Median_Age_ACS_15_19, na.rm = TRUE)) %>%
  select(Median_Age_ACS_15_19, TYPE, Median) %>% 
  mutate(TYPE = factor(TYPE,  levels = c("LOCAL", "COUNTY",
                                        "STATE", "FEDERAL",
                                        "MULTI", "NOT AVAILABLE"))) %>%
  ggplot(aes(x = Median_Age_ACS_15_19)) + 
  geom_histogram() +
  geom_vline(aes(xintercept = Median), color = "darkred") +
  facet_wrap(~TYPE, scales = "free_y") +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Median Age",
       subtitle = "Median of the Distribution Marked in Red\n2015 – 2019 5-year ACS sample data ",
       title = "Distribution of Median Age in Block Groups Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5))

##### distribution of median age faceted by facility security level
prison_boundaries_block_census %>%
  group_by(SECURELVL) %>%
  mutate(Median = median(Median_Age_ACS_15_19, na.rm = TRUE)) %>% 
  select(Median_Age_ACS_15_19, SECURELVL, Median) %>% 
  mutate(SECURELVL = factor(SECURELVL,  levels = c("JUVENILE", "MINIMUM",
                                        "MEDIUM", "MAXIMUM",
                                        "CLOSE", "NOT AVAILABLE"))) %>%
  ggplot(aes(x = Median_Age_ACS_15_19)) + 
  geom_histogram() +
  geom_vline(aes(xintercept = Median), color = "darkred") +
  facet_wrap(~SECURELVL, scales = "free_y") +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Median Age",
       subtitle = "2015 – 2019 5-year ACS sample data ",
       title = "Distribution of Median Age in Block Groups Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5))

Interpretation note on the faceted plots: for a value on the x-axis in a category, we would interpret the corresonding y-value as the number of block groups with that type (or security level) of facility with the specified median age given by the x-value.

Example: The number of block groups containing a federal facility where the median age was about 60 is about 2.

Distribution of the Percentage Urbanized

As defined in the documentation, the variable definitions are as follows:
* pct_URBANIZED_AREA_POP_CEN_2010: “percentage of the 2010 Census total population that lives in a densely settled area containing 50,000 or more people”
* pct_URBAN_CLUSTER_POP_CEN_2010: “percentage of the 2010 Census total population that lives in a densely settled area containing 2,500 to 49,999 people”
* pct_RURAL_POP_CEN_2010: “percentage of the 2010 Census total population that lives outside of an urbanized area or urban cluster”

Distribution of Percentage Urbanized in Block Groups
Data from the 2010 Census
Mean Median
Block Groups in the U.S. 69% 100%
Block Groups Where Facilities are Located 33.7% 0%

Distribution of Percentage Urbanized in Block Groups Where Facilities are Located
by Facility Type
Data from the 2010 Census
Facility Type Mean Median
LOCAL 71% 100%
COUNTY 28.32% 0%
STATE 36.87% 0%
FEDERAL 42.97% 0%
MULTI 35.57% 0%
NOT AVAILABLE 38.48% 0%

Distribution of the Percentage Urbanized in Block Groups Where Facilities are Located
by Facility Security Level
Data from the 2010 Census
Facility Security Level Mean Median
JUVENILE 52.68% 75.235%
MINIMUM 44.47% 0%
MEDIUM 27.53% 0%
MAXIMUM 49.72% 52.08%
CLOSE 25.1% 0%
NOT AVAILABLE 23.17% 0%

Distribution of Percentage Rural

pct_RURAL_POP_CEN_2010: “percentage of the 2010 Census total population that lives outside of an urbanized area or urban cluster”

Distribution of the Percentage Rural in Block Groups
Data from the 2010 Census
Mean Median
Block Groups in the U.S. 20.7% 0%
Block Groups Where Facilities are Located 35.4% 8.87%

Distribution of Percentage Rural in Block Groups Where Facilities are Located
by Facility Type
Data from the 2010 Census
Facility Type Mean Median
LOCAL 14.56% 0%
COUNTY 34.05% 8.25%
STATE 41.15% 20.795%
FEDERAL 29.7% 4.985%
MULTI 44.34% 32.155%
NOT AVAILABLE 45.39% 33.495%

Distribution of Percentage Rural in Block Groups Where Facilities are Located
by Facility Security Level
Data from the 2010 Census
Facility Security Level Mean Median
JUVENILE 28.21% 0.7%
MINIMUM 34.74% 7.75%
MEDIUM 43.85% 28.71%
MAXIMUM 26.49% 0%
CLOSE 48.8% 35.96%
NOT AVAILABLE 36.68% 10.88% 
#### Y-AXIS REPRESENTING FACILITIES RATHER THAN BLOCK GROUPS


##### percentage urbanized 
prison_boundaries_block_census %>% 
  mutate(Median = median(pct_URBANIZED_AREA_POP_CEN_2010, na.rm = TRUE),
         Mean = mean(pct_URBANIZED_AREA_POP_CEN_2010, na.rm = TRUE)) %>%
  select(pct_URBANIZED_AREA_POP_CEN_2010, Median, Mean) %>% 
  ggplot(aes(x = pct_URBANIZED_AREA_POP_CEN_2010 )) + 
  geom_histogram(alpha = .9) +
  geom_vline(aes(xintercept = Median), 
             color = "darkred", 
             size = 1.2) +
  geom_vline(aes(xintercept =Mean), 
             color = "#718BCE",
              size = 1.2) +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Urbanized",
       subtitle = "Mean in Blue, Median in Red\n2010 Census Data",
       title = "Distribution of the Percentage Urbanized Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  ylim(0, 4100)


#  percentage rural 
prison_boundaries_block_census %>% 
  mutate(Median = median(pct_RURAL_POP_CEN_2010, na.rm = TRUE),
         Mean = mean(pct_RURAL_POP_CEN_2010, na.rm = TRUE)) %>%
  select(pct_RURAL_POP_CEN_2010, Median, Mean) %>% 
  ggplot(aes(x = pct_RURAL_POP_CEN_2010 )) + 
  geom_histogram() +
  geom_vline(aes(xintercept = Median), 
             color = "darkred",
             size = 1.2) +
  geom_vline(aes(xintercept =Mean), 
             color = "#718BCE",
              size = 1.2) +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Rural",
       subtitle = "Mean in Blue, Median in Red\n2010 Census data ",
       title = "Distribution of the Percentage Rural Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  ylim(0, 4100)
Percentage of Facilities in a Block Group that was Mostly Rural or Mostly Urban
Rural block groups are defined here as those where greater than 50% of the 2010 Census total population for that block group
was recorded to live outside of an urbanized area or urban cluster
Data from the 2010 Census
Density Percentage
Rural 31.56%
Urban 67.77%
Missing 0.67%
For Block Groups in the United States: Mean and Median Percentages in Urbanized, Urban Cluster, and Rural Areas
Data from the 2010 Census
Population Density Category Mean Percentage Median Percentage
Urban 69.00312 100
Urban Cluster 10.30077 0
Rural 20.69611 0
For Block Groups Where Facilities are Located: Mean and Median Percentages in Urbanized, Urban Cluster, and Rural Areas
Data from the 2010 Census
Population Density Category Mean Median
Urban 33.68880 0.00
Urban Cluster 30.94124 0.00
Rural 35.36997 8.87

We can also see how the distribution of age varies depending on whether blocks are primarily urban or rural, among different facility types, and among different facility security levels.

Distribution of Racial Percentages for each Racial Category

# 
#  mutate(sum_all = Hispanic_ACS_15_19 + NH_AIAN_alone_ACS_15_19 + NH_Asian_alone_ACS_15_19 + NH_Blk_alone_ACS_15_19 + NH_NHOPI_alone_ACS_15_19 + NH_SOR_alone_ACS_15_19 + NH_White_alone_ACS_15_19) %>% View()

# for race reference csv, copied and pasted definitions at link here https://api.census.gov/data/2021/pdb/blockgroup/variables.html into excel file 
race_ref <- read_csv("./data/races_reference.csv", col_names = FALSE) %>% 
  select(X1, X2) %>% 
  rename(`Racial Category` = X1,
         Description = X2) %>%
  mutate(Description = str_replace(Description, "in the ACS", ""),
         Description = str_replace(Description, ",", ",\n")) %>%
  filter(!str_detect(Description, "Census") & !str_detect(Description, "MOE") )

# for full U.S.
blockgroup_level_data %>% 
  select(
         Hispanic_ACS_15_19, NH_AIAN_alone_ACS_15_19, 
         NH_Asian_alone_ACS_15_19, NH_Blk_alone_ACS_15_19, 
         NH_NHOPI_alone_ACS_15_19, NH_SOR_alone_ACS_15_19,
         NH_White_alone_ACS_15_19, Tot_Population_ACS_15_19) %>%
  mutate(across(1:ncol(.), ~ (.x / Tot_Population_ACS_15_19) * 100)) %>% # get percentage by dividing by total for that block
  select(-Tot_Population_ACS_15_19) %>%
  pivot_longer(1:ncol(.), names_to = "Racial Category", values_to  ="Percentage") %>%
  left_join(race_ref, by = c("Racial Category" = "Racial Category")) %>%
  group_by(Description) %>%
  mutate(Median = median(Percentage, na.rm = TRUE),
         Mean = mean(Percentage, na.rm = TRUE)) %>%
  ggplot(aes(x = Percentage)) + 
  geom_histogram(alpha = .9) + 
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE") +
  facet_wrap(~Description, scales = "free_y", ncol = 2) +
  scale_x_continuous(breaks = seq(0, 100, by = 10)) +
  theme_bw() + 
  labs(y = "Number of Block Groups", 
       x = "Percentage",
       subtitle = "Median in Red, Mean in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of Racial Percentages in Block Groups in the U.S.") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  scale_y_continuous(labels = comma)

prison_race <- prison_boundaries_block_census %>% 
  select(FACILITYID, TYPE, SECURELVL,
         Hispanic_ACS_15_19, NH_AIAN_alone_ACS_15_19, 
         NH_Asian_alone_ACS_15_19, NH_Blk_alone_ACS_15_19, 
         NH_NHOPI_alone_ACS_15_19, NH_SOR_alone_ACS_15_19,
         NH_White_alone_ACS_15_19, Tot_Population_ACS_15_19, 
         STATE, COUNTY, TRACTCE10, block_group) %>%
  mutate(across(4:10, ~ (.x / Tot_Population_ACS_15_19) * 100)) %>%  # get percentage by dividing by total for that block
  select(-Tot_Population_ACS_15_19) %>%
  pivot_longer(4:10, names_to = "Racial Category",
               values_to  ="Percentage") %>%
  left_join(race_ref, by = c("Racial Category" = "Racial Category"))


# note that all block groups are represented in each facet, since each block group has a percentage for each racial category # for block groups where facilities are located 
prison_race %>% 
  group_by(STATE, COUNTY, TRACTCE10,
           block_group,  `Racial Category`, 
           Description, Percentage) %>%
  summarize(n=n()) %>%
  ungroup() %>% 
  group_by(Description) %>%
  mutate(Median = median(Percentage, na.rm = TRUE),
         Mean = mean(Percentage, na.rm = TRUE)) %>% 
  ggplot(aes(x = Percentage)) + 
  geom_histogram(alpha = .9) + 
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE") +
  facet_wrap(~Description, scales = "free_y", ncol = 2) +
  scale_x_continuous(breaks = seq(0, 100, by = 10)) +
  theme_bw() + 
  labs(y = "Number of Block Groups", 
       x = "Percentage",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of Racial Percentages in Block Groups Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5))

###### tables for comparison


us <- blockgroup_level_data %>% 
  select(
         Hispanic_ACS_15_19, NH_AIAN_alone_ACS_15_19, 
         NH_Asian_alone_ACS_15_19, NH_Blk_alone_ACS_15_19, 
         NH_NHOPI_alone_ACS_15_19, NH_SOR_alone_ACS_15_19,
         NH_White_alone_ACS_15_19, Tot_Population_ACS_15_19) %>%
  mutate(across(1:ncol(.), ~ (.x / Tot_Population_ACS_15_19) * 100)) %>% # get percentage by dividing by total for that block
  select(-Tot_Population_ACS_15_19) %>%
  pivot_longer(1:ncol(.), 
               names_to = "Racial Category",
               values_to  ="Percentage") %>%
  left_join(race_ref, by = c("Racial Category" = "Racial Category")) %>%
  group_by(Description) %>%
  summarize(Median = median(Percentage, na.rm = TRUE),
         Mean = mean(Percentage, na.rm = TRUE)) %>% 
  rename(`Median for Block Groups in the U.S.` = Median,
         `Mean for Block Groups in the U.S.` = Mean) %>%
  mutate(Description = str_replace_all(Description, "\\n", "<br>"))



fac <- prison_race %>% 
  group_by(STATE, COUNTY, TRACTCE10,
           block_group,  `Racial Category`, 
           Description, Percentage) %>%
  summarize(n=n()) %>%
  ungroup() %>% 
  group_by(Description) %>%
  summarize(Median = median(Percentage, na.rm = TRUE),
         Mean = mean(Percentage, na.rm = TRUE)) %>% 
  rename(`Median for Block Groups<br>Where Facilities are Located` = Median,
         `Mean for Block Groups<br>Where Facilities are Located` = Mean,
         Description2 = Description) 


  
cbind(us, fac) %>% 
  select(-Description2, Description, 
         `Median for Block Groups in the U.S.`,
         `Median for Block Groups<br>Where Facilities are Located`,
         `Mean for Block Groups in the U.S.`,
        `Mean for Block Groups<br>Where Facilities are Located` ) %>% 
  mutate(across(2:5, ~paste0(round(., digits = 2), "%")))  %>%
  kbl(caption = "<span style = 'color:black;'><b>Distribution of the Percentages for Each Racial Category in Block Groups</b></span><br><i>Data from the 2015-2019 ACS</i>", 
      align = "l", escape = FALSE) %>%
  kable_material(c("striped", "hover"), full_width = F) %>%
  row_spec(0, background = "#D9DFEE")  %>% 
  column_spec(1:2, width = "2cm")
Distribution of the Percentages for Each Racial Category in Block Groups
Data from the 2015-2019 ACS
Description Median for Block Groups in the U.S. Mean for Block Groups in the U.S. Median for Block Groups
Where Facilities are Located 		Mean for Block Groups
Where Facilities are Located
American Indian and Alaska Native alone,
not Hispanic or Latino population 		0% 0.72% 0% 1.61%
Asian alone,
not Hispanic or Latino population 		0.65% 4.63% 0% 1.83%
Black or African American alone,
not Hispanic or Latino population 		2.55% 12.89% 7.03% 17.2%
Native Hawaiian and Other Pacific Islander alone,
not Hispanic or Latino population 		0% 0.15% 0% 0.16%
Persons of Hispanic Origin 6.69% 17.2% 5.92% 14.43%
Some other race alone,
not Hispanic or Latino population 		0% 0.23% 0% 0.16%
White alone,
not Hispanic or Latino population 		71.34% 61.82% 67.77% 62.16%

Distribution of Percentage without Health Insurance

As defined in the documentation, No_Health_Ins_ACS_15_19 is the “Number of people who have no health insurance coverage, public or private, in the ACS population”. To find the percentage lacking health insurance, we can divide by the total population, Tot_Population_ACS_15_19.

Distribution of the Percentage Without Health Insurance in Block Groups
Data from the 2010 Census
Mean Median
Block Groups in the U.S. 8.62% 6.2%
Block Groups Where Facilities are Located 8.4% 6.59%

Distribution of the Percentage Without Health Insurance in Block Groups, By Facility Type
Data from the 2015 - 2019 ACS
Facility Type Mean Median
LOCAL 11.46% 9.31%
COUNTY 9.05% 7.39%
STATE 6.67% 4.75%
FEDERAL 5.03% 3.2%
MULTI 7.86% 5.82%
NOT AVAILABLE 12.5% 8.26%

Distribution of the Percentage Without Health Insurance in Block Groups, By Security Level
Data from the 2015 - 2019 ACS
Security Level Mean Median
JUVENILE 8.45% 6.25%
MINIMUM 7.14% 5.62%
MEDIUM 5.14% 2.93%
MAXIMUM 5.87% 3.81%
CLOSE 5.77% 3.94%
NOT AVAILABLE 9.52% 7.75% 
### BY FACILITY, WHERE Y-AXIS IS NUMBER OF FACILITIES RATHER THAN BLOCK GROUPS 


# faceted by facility type 
prison_boundaries_block_census %>%
  mutate(pct_no_insurance = (No_Health_Ins_ACS_15_19 / Tot_Population_ACS_15_19) * 100) %>%
  group_by(TYPE) %>%
  mutate(
          Mean = mean(pct_no_insurance, na.rm = TRUE),
         Median = median(pct_no_insurance, na.rm = TRUE),
         TYPE = factor(TYPE,  levels = c("LOCAL", "COUNTY",
                                        "STATE", "FEDERAL",
                                        "MULTI", "NOT AVAILABLE"))) %>%
  ggplot(aes(x = pct_no_insurance)) +
  geom_histogram() + 
  facet_wrap(~TYPE, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE")  +
  theme_bw() + 
  labs(y = "Number of Block Groups", 
       x = "Percentage Lacking Health Insurance",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Lacking Health Insurance in Block Groups Where Facilities are Located\nFaceted By Facility Type") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  xlim(0,100)


# faceted by security level
prison_boundaries_block_census %>%
  mutate(pct_no_insurance = (No_Health_Ins_ACS_15_19 / Tot_Population_ACS_15_19) * 100) %>%
  group_by(SECURELVL) %>%
  mutate(
         Mean = mean(pct_no_insurance, na.rm = TRUE),
         Median = median(pct_no_insurance, na.rm = TRUE),
         SECURELVL = factor(SECURELVL,  levels = c("JUVENILE", "MINIMUM",
                                        "MEDIUM", "MAXIMUM",
                                        "CLOSE", "NOT AVAILABLE"))) %>%
  ggplot(aes(x = pct_no_insurance)) +
  geom_histogram() + 
  facet_wrap(~SECURELVL, scales = "free_y", ncol = 2) +
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE")  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Lacking Health Insurance",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Lacking Health Insurance in Block Groups Where Facilities are Located\nFaceted By Facility Type") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  xlim(0,100)

Distribution of Percentages Below the Poverty Level

Referencing the documentation:

Prs_Blw_Pov_Lev_ACS_15_19 is defined as the “Number of people classified as below the poverty level given their total family or household income within the last year, family size, and family composition in the ACS population”. To calculate the percentage, we can use the Pov_Univ_ACS_15_19 variable, which is defined as the “Population for whom poverty level is determined”.

Distribution of the Percentage Below the Poverty Line in Block Groups
Data from the 2015 - 2019 ACS
Mean Median
Block Groups in the U.S. 14.88% 10.45%
Block Groups Where Facilities are Located 20% 15.64%

Distribution of the Percentage Below the Poverty Line in Block Groups, By Facility Type
Data from the 2015 - 2019 ACS
Facility Type Mean Median
LOCAL 21.96% 18.15%
COUNTY 20.06% 16.23%
STATE 19.71% 14.45%
FEDERAL 18.6% 14.63%
MULTI 25% 23.34%
NOT AVAILABLE 21.38% 15.34%

Distribution of the Percentage Below the Poverty Line in Block Groups, By Facility Security Level
Data from the 2015 - 2019 ACS
Facility Security Level Mean Median
JUVENILE 19.32% 14.31%
MINIMUM 21.34% 15.87%
MEDIUM 19.17% 14.45%
MAXIMUM 20.7% 15.27%
CLOSE 20.01% 15.05%
NOT AVAILABLE 20.14% 16.48% 
#### BY FACILITY TYPE -- Y-AXIS REPRESENTS NUMBER OF FACILITIES 
# for block groups where facilities are located 
prison_boundaries_block_census %>%
  mutate(pct_below_pov = (Prs_Blw_Pov_Lev_ACS_15_19 / Pov_Univ_ACS_15_19) * 100,
         Mean = mean(pct_below_pov, na.rm = TRUE),
         Median = median(pct_below_pov, na.rm = TRUE)) %>% 
  ggplot(aes(x = pct_below_pov)) +
  geom_histogram(alpha = .9) + 
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Below the Poverty Line",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Below the Poverty Line in Block Groups Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  xlim(-5,100)


# by facility type 
prison_boundaries_block_census %>%
  mutate(pct_below_pov = (Prs_Blw_Pov_Lev_ACS_15_19 / Pov_Univ_ACS_15_19) * 100,
         TYPE = factor(TYPE,  levels = c("LOCAL", "COUNTY",
                                        "STATE", "FEDERAL",
                                        "MULTI", "NOT AVAILABLE"))) %>% 
  group_by(TYPE) %>%
  mutate(Mean = mean(pct_below_pov, na.rm = TRUE),
         Median = median(pct_below_pov, na.rm = TRUE)) %>%
  ggplot(aes(x = pct_below_pov)) +
  geom_histogram(alpha = .9) + 
  facet_wrap(~TYPE, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Below the Poverty Line",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Below the Poverty Line in Block Groups Where Facilities are Located\nFaceted by Facility Type") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  xlim(-5,100)
  
# by security level 
prison_boundaries_block_census %>%
  mutate(pct_below_pov = (Prs_Blw_Pov_Lev_ACS_15_19 / Pov_Univ_ACS_15_19) * 100, 
         SECURELVL = factor(SECURELVL,  levels = c("JUVENILE", "MINIMUM",
                                        "MEDIUM", "MAXIMUM",
                                        "CLOSE", "NOT AVAILABLE"))) %>% 
  group_by(SECURELVL) %>%
  mutate(Mean = mean(pct_below_pov, na.rm = TRUE),
         Median = median(pct_below_pov, na.rm = TRUE)) %>%
  ggplot(aes(x = pct_below_pov)) +
  geom_histogram(alpha = .9) + 
  facet_wrap(~SECURELVL, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Below the Poverty Line",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Below the Poverty Line in Block Groups Where Facilities are Located\nFaceted by Security Level") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  xlim(-5,100)

Distribution of the Percentage of Housing Units Rented

Renter_Occp_HU_ACS_15_19 is defined as “Number of ACS occupied housing units that are not owner occupied, whether they are rented or occupied without payment of rent”.

Distribution of the Percentage of Occupied Housing Units Not Owner Occupied in Block Groups
Data from the 2015 - 2019 ACS
Mean Median
Block Groups in the U.S. 35.51% 29.47%
Block Groups Where Facilities are Located 40.9% 36.18%

Distribution of Occupied Housing Units Not Owner Occupied in Block Groups, By Facility Type
Data from the 2015 - 2019 ACS
Facility Type Mean Median
LOCAL 56.42% 56%
COUNTY 41.89% 38.07%
STATE 35.59% 29.26%
FEDERAL 42.36% 33.62%
MULTI 41.69% 31.98%
NOT AVAILABLE 35.46% 33.69%

Distribution of Occupied Housing Units Not Owner Occupied in Block Groups, By Security Level
Data from the 2015 - 2019 ACS
Facility Security Level Mean Median
JUVENILE 40.2% 35.79%
MINIMUM 41.35% 34.17%
MEDIUM 33.26% 27.6%
MAXIMUM 44.18% 38.44%
CLOSE 31.54% 26.34%
NOT AVAILABLE 41.89% 38.61% 
#### BY FACILITY, WHERE Y-AXIS REPRESENTS NUMBER OF FACILITIES RATHER THAN BLOCK GROUPS 

prison_boundaries_block_census %>%
  mutate(pct_rented = (Renter_Occp_HU_ACS_15_19 / Tot_Occp_Units_ACS_15_19) * 100,
         Mean = mean(pct_rented, na.rm = TRUE),
         Median = median(pct_rented, na.rm = TRUE)) %>% 
  ggplot(aes(x = pct_rented)) +
  geom_histogram(alpha = .9) + 
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage of Housing Units Rented",
       subtitle = "Median Percentage in Red, Mean Percentage in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage of Occupied Housing Units Not Owner Occupied\n in Block Groups Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  xlim(-5,100)



# by facility type 
prison_boundaries_block_census %>%
  mutate(pct_rented = (Renter_Occp_HU_ACS_15_19 / Tot_Occp_Units_ACS_15_19) * 100,
         TYPE = factor(TYPE,  levels = c("LOCAL", "COUNTY",
                                        "STATE", "FEDERAL",
                                        "MULTI", "NOT AVAILABLE"))) %>% 
  group_by(TYPE) %>%
  mutate(Mean = mean(pct_rented, na.rm = TRUE),
         Median = median(pct_rented, na.rm = TRUE)) %>%
  ggplot(aes(x = pct_rented)) +
  geom_histogram(alpha = .9) + 
  facet_wrap(~TYPE, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage of Housing Units Rented",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage of Occupied Housing Units Not Owner Occupied\n in Block Groups Where Facilities are Located\nFaceted by Facility Type") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  xlim(-5,100)


# by security level 
prison_boundaries_block_census %>%
  mutate(pct_rented = (Renter_Occp_HU_ACS_15_19 / Tot_Occp_Units_ACS_15_19) * 100, 
         SECURELVL = factor(SECURELVL,  levels = c("JUVENILE", "MINIMUM",
                                        "MEDIUM", "MAXIMUM",
                                        "CLOSE", "NOT AVAILABLE"))) %>% 
  group_by(SECURELVL) %>%
  mutate(Mean = mean(pct_rented, na.rm = TRUE),
         Median = median(pct_rented, na.rm = TRUE)) %>%
  ggplot(aes(x = pct_rented)) +
  geom_histogram(alpha = .9) + 
  facet_wrap(~SECURELVL, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage of Housing Units Rented",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage of Occupied Housing Units Not Owner Occupied\n in Block Groups Where Facilities are Located\nFaceted by Security Level") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) +
  xlim(-5,100)

Distribution of Average Household Size

Tot_Prns_in_HHD_ACS_15_19 is defined as the “All persons (household population) in ACS occupied housing unit. This is the numerator to calculate the average household size”. The denominator used to calculate the average household size is Tot_Occp_Units_ACS_15_19.

Distribution of the Average Household Size in Block Groups (Units are People)
Data from the 2015 - 2019 ACS
Mean Median
Block Groups in the U.S. 2.64 2.57
Block Groups Where Facilities are Located 2.50 2.41

Distribution of Percentage Institutionalized

pct_Inst_GQ_CEN_2010 is defined as “The percentage of the 2010 Census population who live in group quarters and are primarily ineligible, unable, or unlikely to participate in labor force while residents. Institutional group quarters include correctional facilities for adults, juvenile facilities, nursing facilities, and other institutional facilities.”

Distribution of the Percentage in Institutionalized Group Quarters in Block Groups
Data from the 2010 Census
Mean Median
Block Groups in the U.S. 1.04% 0%
Block Groups Where Facilities are Located 19.94% 8.33%

Distribution of the Percentage Living in Institutional Group Quarters in Block Groups, By Facility Type
Data from the 2010 Census
Facility Type Mean Median
LOCAL 7.88% 0.74%
COUNTY 14.13% 7.1%
STATE 34.09% 21.39%
FEDERAL 54.2% 55.56%
MULTI 39.7% 32.98%
NOT AVAILABLE 7.05% 1.71%

Distribution of the Percentage Living in Institutional Group Quarters in Block Groups, By Facility Security Level
Data from the 2010 Census
Facility Security Level Mean Median
JUVENILE 15.04% 5.83%
MINIMUM 30.11% 16.72%
MEDIUM 52.74% 53.98%
MAXIMUM 42.82% 35.51%
CLOSE 57.37% 55.32%
NOT AVAILABLE 12.91% 5.96% 
##### BY FACILITY RATHER THAN BY BLOCK GROUP; Y-AXIS REPRESENTS NUMBER OF FACILITIES

# distribution of the Percentage Living in Institutional Group Quarters in block groups where facilities are
prison_boundaries_block_census %>%
  mutate(
         Mean = mean(pct_Inst_GQ_CEN_2010, na.rm = TRUE),
         Median = median(pct_Inst_GQ_CEN_2010, na.rm = TRUE)) %>% 
  ggplot(aes(x = pct_Inst_GQ_CEN_2010)) +
  geom_histogram(alpha = .9) + 
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage in Institutional Group Quarters",
       subtitle = "Median Percentage in Red, Mean Percentage in Blue\n2010 Census data",
       title = "Distribution of the Percentage Living in Institutional Group Quarters\nin Block Groups Where Facilities Are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  scale_x_continuous(breaks = seq(0, 100, by = 5), 
                     limits = c(-3,105)) 


# distribution of the Percentage Living in Institutional Group Quarters in block groups where facilities are,
# faceted by facility type 
prison_boundaries_block_census %>%
  group_by(TYPE) %>%
  mutate(
         Mean = mean(pct_Inst_GQ_CEN_2010, na.rm = TRUE),
         Median = median(pct_Inst_GQ_CEN_2010, na.rm = TRUE),
         TYPE = factor(TYPE,  levels = c("LOCAL", "COUNTY",
                                        "STATE", "FEDERAL",
                                        "MULTI", "NOT AVAILABLE"))) %>% 
  ggplot(aes(x = pct_Inst_GQ_CEN_2010)) +
  geom_histogram(alpha = .9) + 
  facet_wrap(~TYPE, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage in Institutional Group Quarters",
       subtitle = "Median Percentage in Red, Mean Percentage in Blue\n2010 Census data",
       title = "Distribution of the Percentage Living in Institutional Group Quarters\nin Block Groups Where Facilities Are Located\nFaceted by Facility Type") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  scale_x_continuous(breaks = seq(0, 100, by = 20), 
                     limits = c(-3,105)) 



# distribution of the Percentage Living in Institutional Group Quarters in block groups where facilities are,
# faceted by security level 
prison_boundaries_block_census %>%
  group_by(SECURELVL) %>%
  mutate(
         Mean = mean(pct_Inst_GQ_CEN_2010, na.rm = TRUE),
         Median = median(pct_Inst_GQ_CEN_2010, na.rm = TRUE),
         SECURELVL = factor(SECURELVL,  levels = c("JUVENILE", "MINIMUM",
                                        "MEDIUM", "MAXIMUM",
                                        "CLOSE", "NOT AVAILABLE"))) %>% 
  ggplot(aes(x = pct_Inst_GQ_CEN_2010)) +
  geom_histogram(alpha = .9) + 
  facet_wrap(~SECURELVL, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred", size = 1.2) +
  geom_vline(aes(xintercept = Mean), color = "#718BCE", size = 1.2)  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage in Institutional Group Quarters",
       subtitle = "Median Percentages in Red, Mean Percentages in Blue\n2010 Census data",
       title = "Distribution of the Percentage Living in Institutional Group Quarters\nin Block Groups Where Facilities Are Located\nFaceted by Security Level") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  scale_x_continuous(breaks = seq(0, 100, by = 20), 
                     limits = c(-3,105))

Distribution of the Percentage Living in Institutional Group Quarters in Block Groups, By State
Data from the 2010 Census
STATE Mean Median
AK 9.73% 5.14%
AL 15.16% 7.26%
AR 15.04% 7.25%
AZ 36.06% 19.95%
CA 24.86% 7.96%
CO 21.88% 9.91%
CT 55.18% 62.98%
DC 29.74% 2.95%
DE 35.23% 18.73%
FL 27.29% 14.52%
GA 17.42% 9.05%
GU NaN% NA%
HI 37.13% 24.39%
IA 8.28% 3.54%
ID 8.93% 3.66%
IL 19.19% 6.96%
IN 16.93% 9.82%
KS 9.94% 2.97%
KY 18.41% 12.67%
LA 19.68% 10.96%
MA 30.77% 23.45%
MD 29.04% 14.17%
ME 11.99% 12.28%
MI 22.38% 9.3%
MN 9.78% 3.58%
MO 13.84% 6.08%
MS 15.2% 7.48%
MT 10.35% 2.86%
NC 20.57% 13.87%
ND 8.48% 4.8%
NE 9.44% 3.29%
NH 19.46% 12.3%
NJ 32.54% 20.87%
NM 22.69% 13.38%
NV 17.41% 4.19%
NY 27.14% 9.52%
OH 20.59% 9.88%
OK 18.12% 8.94%
OR 19.61% 9.01%
PA 32.33% 21.94%
PR 27.01% 10.54%
RI 31.91% 14.62%
SC 26.78% 15.1%
SD 11.01% 4.98%
TN 17.57% 8.96%
TX 23.26% 7.49%
UT 11.81% 5.95%
VA 26.47% 14.17%
VI NaN% NA%
VT 11.99% 11.54%
WA 19.15% 6.24%
WI 19.71% 10.92%
WV 11.02% 3.32%
WY 8.45% 5.34%

Distribution of the Percentage Receiving Public Assistance Income

pct_PUB_ASST_INC_ACS_15_19 is the “percentage of all ACS occupied housing units that receive public assistance income (general assistance and Temporary Assistance to Needy Families).” There are two main categories of public assistance programs, social welfare programs and social insurance programs. Social welfare programs generally have low income criteria for eligibility, and include programs such as Supplemental Security Income (SSI), Supplemental Nutrition Assistance Program (SNAP), Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Temporary Assistance for Needy Families (TANF), and General Assistance (GA). Social insurance programs, meanwhile, are based on criteria outside income, such as age, employment status, or being a veteran. These include Social security (self and on behalf of a dependent child), Department of Veterans’ Affairs benefits (except Veteran’s pension), Unemployment insurance compensation, and Workers’ compensation.

Distribution of the Percentage Receiving Public Assistance Income in Block Groups
Data from the 2010 Census
Mean Median
Block Groups in the U.S. 2.65% 1.06%
Block Groups Where Facilities are Located 2.89% 1.45%

Distribution of the Percentage Receiving Public Assistance Income in Block Groups, By Facility Type
Data from the 2010 Census
Facility Type Mean Median
LOCAL 3.79% 2.05%
COUNTY 2.85% 1.48%
STATE 2.75% 1.34%
FEDERAL 2.29% 0%
MULTI 3.27% 2.19%
NOT AVAILABLE 4.26% 0.74%

Distribution of the Percentage Receiving Public Assistance Income in Block Groups, By Facility Security Level
Data from the 2010 Census
Facility Security Level Mean Median
JUVENILE 3.22% 1.57%
MINIMUM 2.92% 1.5%
MEDIUM 2.47% 0.94%
MAXIMUM 3.01% 1.25%
CLOSE 2.18% 0.61%
NOT AVAILABLE 2.82% 1.48% 
##### BY FACILITY, WHERE Y-AXIS IS THE NUMBER OF FACILITIES


# distribution of percentage receiving public assistance income in block groups where facilities are located 
prison_boundaries_block_census %>%
  mutate(
         Mean = mean(pct_PUB_ASST_INC_ACS_15_19, na.rm = TRUE),
         Median = median(pct_PUB_ASST_INC_ACS_15_19, na.rm = TRUE)) %>% 
  ggplot(aes(x = pct_PUB_ASST_INC_ACS_15_19)) +
  geom_histogram(alpha = .9) + 
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE")  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Receiving Public Assistance Income",
       subtitle = "Median Percentage in Red, Mean Percentage in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Receiving Public Assistance Income\nin Block Groups Where Facilities are Located") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  scale_x_continuous(breaks = seq(0, 100, by = 5), limits = c(-3,105)) 


# distribution of percentage receiving public assistance income in block groups where facilities are located
# faceted by facility type 
prison_boundaries_block_census %>% 
  group_by(TYPE) %>% 
  mutate(
         Mean = mean(pct_PUB_ASST_INC_ACS_15_19, na.rm = TRUE),
         Median = median(pct_PUB_ASST_INC_ACS_15_19, na.rm = TRUE)) %>% 
  ggplot(aes(x = pct_PUB_ASST_INC_ACS_15_19)) +
  geom_histogram(alpha = .9, bins = 40) + facet_wrap(~TYPE, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE")  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Receiving Public Assistance Income",
       subtitle = "Median Percentage in Red, Mean Percentage in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Receiving Public Assistance Income\nin Block Groups Where Facilities are Located\nFaceted by Facility Type") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  scale_x_continuous(breaks = seq(0, 100, by = 10), limits = c(-3,105))


# distribution of percentage receiving public assistance income in block groups where facilities are located
# faceted by security level 
prison_boundaries_block_census %>% 
  group_by(SECURELVL) %>% 
  mutate(
         Mean = mean(pct_PUB_ASST_INC_ACS_15_19, na.rm = TRUE),
         Median = median(pct_PUB_ASST_INC_ACS_15_19, na.rm = TRUE)) %>% 
  ggplot(aes(x = pct_PUB_ASST_INC_ACS_15_19)) +
  geom_histogram(alpha = .9, bins = 40) + 
  facet_wrap(~SECURELVL, scales = "free_y") +
  geom_vline(aes(xintercept = Median), color = "darkred") +
  geom_vline(aes(xintercept = Mean), color = "#718BCE")  +
  theme_bw() + 
  labs(y = "Number of Facilities", 
       x = "Percentage Receiving Public Assistance Income",
       subtitle = "Median Percentage in Red, Mean Percentage in Blue\n2015 – 2019 5-year ACS sample data",
       title = "Distribution of the Percentage Receiving Public Assistance Income\nin Block Groups Where Facilities are Located\nFaceted by Security Level") +
  theme(text = element_text(family = "Optima"),
        plot.subtitle = element_text(face = "italic", hjust = .5),
        plot.title = element_text(face = "bold", hjust = 0.5)) + 
  scale_x_continuous(breaks = seq(0, 100, by = 10), limits = c(-3,105))

Distribution of Percentage Where No Adult Speaks English “Very Well”

pct_ENG_VW_ACS_15_19 is defined as the “percentage of all ACS occupied housing units where no one ages 14 years and over speaks English only or speaks English ‘very well’”.

Distribution of the Percentage Where No One Over 14 in the Household Speaks English ‘Very Well’ in Block Groups
Data from the 2015 - 2019 ACS
Mean Median
Block Groups in the U.S. 5.17% 0%
Block Groups Where Facilities are Located 3.46% 0%

Tract Level Analysis

Some variables are not available at the block group level, but still may be relevant. These include the percentage of the population lacking a computing device, the percentage of the popoulation with broadband internet access and a computing device, and the percentage of the population 16 years and older who are unemployed.

Distribution of Percentages Lacking a Computing Device

The tract level data has additional variables available, and separate documentation outlining the definitions.

The variable pct_Pop_NoCompDevic_ACS_15_19 is defined as the “Percentage of people that live in households that do not have computing device of any kind in the ACS”.

Distribution of the of the Population Without a Computing Device in Tracts
Data from the 2015 - 2019 ACS
Mean Median
Tracts in the U.S. 7.29% 5.56%
Tracts Where Facilities are Located 10.16% 8.8%

Distribution of Percentages With Access to Broadband Wifi

The variable pct_Pop_w_BroadComp_ACS_15_19 is defined as the “Percentage of people that live in households that have both broadband Internet access and a computing device of any kind in the ACS”.

The thresholds for upload and download speeds are set by the Federal Communications Commission, and have changed over time, shaping our view on the digital divides that exist.

Distribution of the Population with Broadband Internet Access in Tracts
Data from the 2015 - 2019 ACS
Mean Median
Tracts in the U.S. 84.63% 87.08%
Tracts Where Facilities are Located 79.68% 81.41%

Distribution of the Percentage Unemployed

The variable pct_Civ_unemp_16p_ACS_15_19 is defined as the “percentage of ACS civilians ages 16 years and over in the labor force that are unemployed”.

Distribution of Civilians Ages 16+ in the Labor Force Who are Unemployed in Tracts
Data from the 2015 - 2019 ACS
Mean Median
Tracts in the U.S. 5.95% 4.8%
Tracts Where Facilities are Located 6.33% 5.2%